"A review of trading book capital rules, due to be launched in March by the Basel Committee on Banking Supervision, will consider ditching value-at-risk as the main measure on which market risk capital is calculated, sources say - but it may not be easy to find a replacement."-- Goodbye VaR? Basel to Consider Other Risk Metrics, Risk.Net, 28 Feb 2012.

Dr. Yogesh Malhotra's Market Risk presentation of January 26, 2012, in which he strongly recommended market risk analysts to start looking beyond VaR and seriously considering Expected Shortfall models preceded subsequent "revelation" on February 28, 2012, by Risk.net that the Basel Committee was considering ditching VaR as a means of calculating market risk capital. Risk.Net reports about its February 28, 2012, article that their "February 2012 article broke the news that the Basel Committee was considering ditching VaR as a means of calculating market risk capital in favour of expected shortfall."

His prior research into computational mathematical financial engineering models linked to the Global Financial Crisis preceded the Crisis by about 4-5 years culminating in his investigation of VaR and related financial risk models. For instance, his prescient reference to the "most technical, numbers-driven, globally popular area of financial markets" of financial engineering in course of related investigation is available in his invited interview published by the UK management press in 2005. Having affirmed the trajectory of his own post-doctoral quantitative risk modeling and risk management research in 2012 with the Columbia University Professor Emanuel Derman, world's most known expert on Model Risks in his view, who was prior MD and head of Quantitative Strategies group at Goldman Sachs, Dr. Malhotra is optimistic about a more enlightened future of quantitative risk modeling.

"The only Constant used to be Change... Even it is not Constant anymore..."— Yogesh MalhotraMore...

Advancing Beyond Limitations of Quantitative Finance Models

"As far as the propositions of mathematics refer to reality they are not certain, and so far as they are certain, they do not refer to reality." — Albert Einstein (1879-1955) U. S. physicist, born in Germany.

“The models, according to finance experts and economists, did fail to keep pace with the explosive growth in complex securities, the resulting intricate web of risk and the dimensions of the danger. But the larger failure, they say, was human — in how the risk models were applied, understood and managed... If the incentives and the systems change, the hard data can mean less than it did or something else than it did…The danger is that the modeling becomes too mechanical….The miss by Wall Street analysts shows how models can be precise out to several decimal places, and yet be totally off base… Indeed, the behavioral uncertainty added to the escalating complexity of financial markets help explain the failure in risk management. The quantitative models typically have their origins in academia and often the physical sciences. In academia, the focus is on problems that can be solved, proved and published — not messy, intractable challenges. In science, the models derive from particle flows in a liquid or a gas, which conform to the neat, crisp laws of physics. Not so in financial modeling. To confuse the model with the world is to embrace a future disaster driven by the belief that humans obey mathematical rules.”

“I began to believe it was possible to apply the methods of physics successfully to economics and finance, perhaps even to build a grand unified theory of securities….After twenty years on Wall Street I’m a disbeliever. The similarity of physics and finance lies more in their syntax than their semantics. In physics you’re playing against God, and He doesn’t change His laws very often. In finance you’re playing against God’s creatures, agents who value assets based on their ephemeral opinions.”

--- Dr. Emanuel Derman, Columbia University Professor, ex-Goldman Head of Quantitative Trading, and author of the book My Life as a Quant in his new book Models Behaving Badly: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life, 2011.

“The complex financial models that got us into this mess too often mask human nature behind false limitations of risk ...Financial theory has tried hard to emulate physics and discover its own elegant, universal laws. But finance and economics are concerned with the human world of monetary value. Markets are made of people who are influenced by events, by their feelings about events, and by their expectations of other people's feelings about events...Financial theories written in mathematical notation - aka models - imply a false sense of precision. Good modelers know that... Financial markets are alive. A model, however beautiful, is an artifice. To confuse the model with the world is to embrace a future disaster in the belief that humans obey mathematical principles.”

“Of course, assets are not really geometric Brownian motions with constant volatility…”“Of course, stock price movements are much more complicated than indicated by the binomial asset-pricing model…”“Of course, the actual probability for the occurrence of any particular [stock price] path is zero…”

“We are now in a position to introduce a very important principle in the pricing of derivatives known as risk-neutral valuation. This states that, when valuing a derivative, we can make the assumption that investors are risk-neutral. This assumption means investors do not increase the expected return they require from an investment to compensate for increased risk. A world where investors are risk-neutral is referred to as a risk-neutral world... The world we live in is, of course, not a risk-neutral world. The higher the risks investors take, the higher the expected returns they require.”